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Add penalty for minimal number of stocks considered in final portfolio #1

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40 changes: 27 additions & 13 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,6 @@
from src.evolution import setup_deap, run_nsga2
from src.plots import (
plot_pareto_vs_markowitz,
plot_portfolio_vs_baseline,
plot_final_portfolio,
plot_performance_summary,
)
from src.utils import optimize_markowitz, maximum_drawdown, sharpe_ratio
Expand Down Expand Up @@ -165,6 +163,7 @@ def main():
exp_id = f"experiment-{now.strftime('%Y%m%d')}-{now.strftime('%H%M%S')}"
output_dir = os.path.join("plots", exp_id)
os.makedirs(output_dir, exist_ok=True)
os.makedirs(os.path.join(output_dir, "portfolios"), exist_ok=True)
print(f"Saving plots to: {output_dir}")

tickers = TICKER_SETS[args.ticker_set]
Expand Down Expand Up @@ -237,6 +236,7 @@ def main():
stock_covariances.values,
historical_returns=historical_returns,
risk_metric=args.risk_metric,
min_positions=min(3, len(stock_names)//2),
)

benchmark_prices = load_benchmark(benchmark_ticker, start_date)
Expand Down Expand Up @@ -282,8 +282,10 @@ def main():
)

pareto_front = tools.sortNondominated(pop, len(pop), first_front_only=True)[0]
best = max(pareto_front, key=lambda ind: ind.fitness.values[0])
print(f"Final best: return={best.fitness.values[0]:.4f}, risk={best.fitness.values[1]:.4f}")
sorted_front = sorted(pareto_front, key=lambda ind: ind.fitness.values[0], reverse=True)
n_top = max(5, int(args.pop_size * 0.1))
top_candidates = sorted_front[:n_top]
print(f"Plotting top {len(top_candidates)} portfolios from the Pareto front...")

plot_pareto_vs_markowitz(
pareto_front,
Expand All @@ -298,15 +300,27 @@ def main():
covariances=stock_covariances.values,
)

plot_performance_summary(
prices,
np.array(best),
np.array(stock_names),
index_prices=benchmark_prices,
title="Final Portfolio Performance",
output_dir=output_dir,
show=not args.no_plots,
)
for i, ind in enumerate(top_candidates):
ret = ind.fitness.values[0]
risk = ind.fitness.values[1]

weights = np.array(ind)
holdings = [(name, w) for name, w in zip(stock_names, weights) if w > 0.1]
holdings.sort(key=lambda x: x[1], reverse=True)

print(f"\nCandidate #{i + 1}: Return={ret:.4f}, Risk={risk:.4f}")
print(f"Top holdings: {', '.join([f'{h[0]}: {h[1] * 100:.1f}%' for h in holdings[:5]])}")

plot_performance_summary(
prices,
weights,
np.array(stock_names),
index_prices=benchmark_prices,
title=f"Rank #{i + 1} Portfolio (Ret: {ret * 100:.1f}%)",
output_dir=output_dir,
show=not args.no_plots,
filename=f"portfolios/final_portfolio_rank_{i + 1:02d}.png"
)


if __name__ == "__main__":
Expand Down
22 changes: 22 additions & 0 deletions src/evolution.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,9 @@ def evaluate_portfolio(
covariances: np.ndarray,
hist_returns: np.ndarray,
metric: str,
min_positions: int = 0,
penalty_factor: float = 0.1,
min_weight: float = 0.01,
):
if np.any(np.isnan(portfolio)) or np.any(np.isinf(portfolio)):
return float("nan"), float("nan")
Expand All @@ -49,6 +52,16 @@ def evaluate_portfolio(
else:
raise ValueError(f"Unknown risk metric: {metric}")

# Add penalty if portfolio is too small
active_positions = np.sum(portfolio > min_weight)
if active_positions < min_positions:
missing = min_positions - active_positions
tax_rate = missing * penalty_factor
penalty_amount = np.abs(portfolio_return) * tax_rate

portfolio_return -= penalty_amount
risk_value += (risk_value * tax_rate)

return portfolio_return, risk_value


Expand Down Expand Up @@ -81,6 +94,9 @@ def setup_deap(
stock_covariances,
historical_returns=None,
risk_metric="std",
min_positions=0,
min_weight=0.01,
penalty_factor=0.1,
mutation_kwargs=None,
crossover_kwargs=None,
):
Expand All @@ -92,6 +108,9 @@ def setup_deap(
stock_covariances: Covariance matrix of stock returns
historical_returns: Historical returns array (n_days, n_assets) - required for mdd/sharpe
risk_metric: Risk metric to use - 'std' (volatility), 'mdd' (max drawdown), or 'sharpe'
min_positions: Minimum number of assets to use for portfolio before adding penalty.
min_weight: Minimum weight threshold for an asset to be considered an "active position" (e.g., 0.01 for 1% of portfolio).
penalty_factor: The penalty multiplier applied to return and risk for each missing position (e.g., 0.1 means 10% penalty per missing asset).
mutation_kwargs: Dictionary of kwargs for the mutation function
crossover_kwargs: Dictionary of kwargs for the crossover function
"""
Expand All @@ -118,6 +137,9 @@ def create_individual(n_assets: int):
covariances=stock_covariances,
hist_returns=historical_returns,
metric=risk_metric,
min_positions=min_positions,
penalty_factor=penalty_factor,
min_weight=min_weight,
)

mut_kwargs = mutation_kwargs or {}
Expand Down